"A 6-sided die is rolled 15 times and the results are: side 1 comes up 0 times; side 2: 1 time; side 3: 2 times; side 4: 3 times; side 5: 4 times; side 6: 5 times. Based on these results, what is the probability of side 3 coming up when using Add-1 Smoothing?",2.0/15,1.0/7,3.0/16,1.0/5,B
Which image data augmentation is most common for natural images?,random crop and horizontal flip,random crop and vertical flip,posterization,dithering,A
"You are reviewing papers for the World’s Fanciest Machine Learning Conference, and you see submissions with the following claims. Which ones would you consider accepting? ",My method achieves a training error lower than all previous methods!,My method achieves a test error lower than all previous methods! (Footnote: When regularisation parameter λ is chosen so as to minimise test error.),My method achieves a test error lower than all previous methods! (Footnote: When regularisation parameter λ is chosen so as to minimise cross-validaton error.),My method achieves a cross-validation error lower than all previous methods! (Footnote: When regularisation parameter λ is chosen so as to minimise cross-validaton error.),C
"To achieve an 0/1 loss estimate that is less than 1 percent of the true 0/1 loss (with probability 95%), according to Hoeffding's inequality the IID test set must have how many examples?",around 10 examples,around 100 examples,between 100 and 500 examples,more than 1000 examples,D
"Traditionally, when we have a real-valued input attribute during decision-tree learning we consider a binary split according to whether the attribute is above or below some threshold. Pat suggests that instead we should just have a multiway split with one branch for each of the distinct values of the attribute. From the list below choose the single biggest problem with Pat’s suggestion:",It is too computationally expensive.,It would probably result in a decision tree that scores badly on the training set and a testset.,It would probably result in a decision tree that scores well on the training set but badly on a testset.,It would probably result in a decision tree that scores well on a testset but badly on a training set.,C